A direct sequence-code division multiple access (DS-CDMA) system distinguishes a user by a signal of a frequency band. Unfortunately, inter-signal interference occurs even among a small number of users and is recognized as multiple access inference (MAI). This noise problem causes a critical increase in bit error rate (BER) under a near/far effect, which is a sensitive issue in the DS-CDMA system.
To mitigate such a problem, a multiuser detection (MUD) technique has been applied to eliminate the interference. In such a technique, a known optimization solving means for the MUD may be acquired through minimizing the means square error (MMSE). Nonetheless, a lot of computational resources and training effort are required for performing the calculation. Obviously, such a method is not suitable for implementation in most communication devices. In order to solve such a problem, many approaches including multilayer perceptron, a support vector machine, a wavelet neuron network, and Gaussian process regression (GPR) are proposed. Of the machine learning approaches, the GPR is considered the most promising tool in terms of flexibility and accuracy.
In fact, a Gaussian process is widely used for prediction and classification in many research areas such as data communications, networking, and signal processing. Rather than determining parameters of a model from a scratch, the Gaussian process can help to adopt the parameters to represent an actual underlying function. As such, the Gaussian process is a suitable choice for noise, corrupted or erroneous data. However, such a method has a disadvantage of high complexity. In a standard implementation, the GPR requires complexity of O(n3) for computation and O(n2) for storage when computing n training points in a dataset. Even in the application of a sparse spectrum Gaussian process, if m is the number of basic functions, the complexity is still O(mn2) for computation and O(nm) for storage.
Accordingly, there is a need for another scheme capable of reducing the complexity of the GPR.